



'''simple docstring'''






import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional

import evaluate
import numpy as np
import torch
from datasets import load_dataset
from PIL import Image
from torchvision.transforms import (
    CenterCrop,
    Compose,
    Normalize,
    RandomHorizontalFlip,
    RandomResizedCrop,
    Resize,
    ToTensor,
)

import transformers
from transformers import (
    MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
    AutoConfig,
    AutoImageProcessor,
    AutoModelForImageClassification,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    set_seed,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version, send_example_telemetry
from transformers.utils.versions import require_version
lowerCAmelCase:   int					=		logging.getLogger(__name__)

# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version('4.31.0')

require_version('datasets>=1.8.0', 'To fix: pip install -r examples/pytorch/image-classification/requirements.txt')

lowerCAmelCase:   Tuple					=		list(MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING.keys())
lowerCAmelCase:   List[Any]					=		tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)
def  lowerCamelCase__			(		_A							):
  with open(_A					,     'rb'							) as f:
    a		:   Dict	  =	Image.open(_A							)
    return im.convert('RGB'							)

@dataclass
class 				a__:
    lowercase__					=       field(
        default=lowerCamelCase__						,					metadata={
            """help""": """Name of a dataset from the hub (could be your own, possibly private dataset hosted on the hub)."""
        }						,					)
    lowercase__					=       field(
        default=lowerCamelCase__						,					metadata={"""help""": """The configuration name of the dataset to use (via the datasets library)."""}      )
    lowercase__					=       field(default=lowerCamelCase__						,					metadata={"""help""": """A folder containing the training data."""}      )
    lowercase__					=       field(default=lowerCamelCase__						,					metadata={"""help""": """A folder containing the validation data."""}      )
    lowercase__					=       field(
        default=0.15						,					metadata={"""help""": """Percent to split off of train for validation."""}      )
    lowercase__					=       field(
        default=lowerCamelCase__						,					metadata={
            """help""": (
                """For debugging purposes or quicker training, truncate the number of training examples to this """
                """value if set."""
            )
        }						,					)
    lowercase__					=       field(
        default=lowerCamelCase__						,					metadata={
            """help""": (
                """For debugging purposes or quicker training, truncate the number of evaluation examples to this """
                """value if set."""
            )
        }						,					)







    def      lowercase_			( self					:    Optional[Any]      ):
      if self.dataset_name is None and (self.train_dir is None and self.validation_dir is None):
        raise ValueError(
            'You must specify either a dataset name from the hub or a train and/or validation directory.'      )

@dataclass
class 				a__:
    lowercase__					=       field(
        default="""google/vit-base-patch16-224-in21k"""						,					metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""}						,					)
    lowercase__					=       field(
        default=lowerCamelCase__						,					metadata={"""help""": """If training from scratch, pass a model type from the list: """ + """, """.join(lowerCamelCase__      )}						,					)
    lowercase__					=       field(
        default=lowerCamelCase__						,					metadata={"""help""": """Pretrained config name or path if not the same as model_name"""}      )
    lowercase__					=       field(
        default=lowerCamelCase__						,					metadata={"""help""": """Where do you want to store the pretrained models downloaded from s3"""}      )
    lowercase__					=       field(
        default="""main"""						,					metadata={"""help""": """The specific model version to use (can be a branch name, tag name or commit id)."""}						,					)
    lowercase__					=       field(default=lowerCamelCase__						,					metadata={"""help""": """Name or path of preprocessor config."""}      )
    lowercase__					=       field(
        default=lowerCamelCase__						,					metadata={
            """help""": (
                """Will use the token generated when running `huggingface-cli login` (necessary to use this script """
                """with private models)."""
            )
        }						,					)
    lowercase__					=       field(
        default=lowerCamelCase__						,					metadata={"""help""": """Will enable to load a pretrained model whose head dimensions are different."""}						,					)
def  lowerCamelCase__			(		_A							):
  a		:   Tuple	  =	torch.stack([example['pixel_values'] for example in examples]							)
  a		:   int	  =	torch.tensor([example['labels'] for example in examples]							)
  return {"pixel_values": pixel_values, "labels": labels}
def  lowerCamelCase__			(		):
  # See all possible arguments in src/transformers/training_args.py
  # or by passing the --help flag to this script.
  # We now keep distinct sets of args, for a cleaner separation of concerns.

  a		:   List[str]	  =	HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)							)
  if len(sys.argv							) == 2 and sys.argv[1].endswith('.json'							):
    # If we pass only one argument to the script and it's the path to a json file,
    # let's parse it to get our arguments.
    a  ,      a  ,      a		:   Dict	  =	parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]							)							)
  else:
    a  ,      a  ,      a		:   Union[str, Any]	  =	parser.parse_args_into_dataclasses()

  # Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
  # information sent is the one passed as arguments along with your Python/PyTorch versions.
  send_example_telemetry('run_image_classification'					,     _A					,     _A							)

  # Setup logging
  logging.basicConfig(
      format='%(asctime)s - %(levelname)s - %(name)s - %(message)s'					,     datefmt='%m/%d/%Y %H:%M:%S'					,     handlers=[logging.StreamHandler(sys.stdout							)]					,     )

  if training_args.should_log:
    # The default of training_args.log_level is passive, so we set log level at info here to have that default.
    transformers.utils.logging.set_verbosity_info()

  a		:   Any	  =	training_args.get_process_log_level()
  logger.setLevel(_A							)
  transformers.utils.logging.set_verbosity(_A							)
  transformers.utils.logging.enable_default_handler()
  transformers.utils.logging.enable_explicit_format()

  # Log on each process the small summary:
  logger.warning(
      f"""Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}"""
      + f"""distributed training: {bool(training_args.local_rank != -1							)}, 16-bits training: {training_args.fpaa}"""							)
  logger.info(f"""Training/evaluation parameters {training_args}"""							)

  # Detecting last checkpoint.
  a		:   Tuple	  =	None
  if os.path.isdir(training_args.output_dir							) and training_args.do_train and not training_args.overwrite_output_dir:
    a		:   int	  =	get_last_checkpoint(training_args.output_dir							)
    if last_checkpoint is None and len(os.listdir(training_args.output_dir							)							) > 0:
      raise ValueError(
          f"""Output directory ({training_args.output_dir}) already exists and is not empty. """
          'Use --overwrite_output_dir to overcome.'							)
    elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
      logger.info(
          f"""Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change """
          'the `--output_dir` or add `--overwrite_output_dir` to train from scratch.'							)

    # Set seed before initializing model.
  set_seed(training_args.seed							)

  # Initialize our dataset and prepare it for the 'image-classification' task.
  if data_args.dataset_name is not None:
    a		:   List[str]	  =	load_dataset(
        data_args.dataset_name					,     data_args.dataset_config_name					,     cache_dir=model_args.cache_dir					,     task='image-classification'					,     use_auth_token=True if model_args.use_auth_token else None					,     )
  else:
    a		:   Any	  =	{}
    if data_args.train_dir is not None:
      a		:   List[Any]	  =	os.path.join(data_args.train_dir					,     '**'							)
    if data_args.validation_dir is not None:
      a		:   Tuple	  =	os.path.join(data_args.validation_dir					,     '**'							)
    a		:   Union[str, Any]	  =	load_dataset(
        'imagefolder'					,     data_files=_A					,     cache_dir=model_args.cache_dir					,     task='image-classification'					,     )

  # If we don't have a validation split, split off a percentage of train as validation.
  a		:   Optional[int]	  =	None if 'validation' in dataset.keys() else data_args.train_val_split
  if isinstance(data_args.train_val_split					,     _A							) and data_args.train_val_split > 0.0:
    a		:   Tuple	  =	dataset['train'].train_test_split(data_args.train_val_split							)
    a		:   Dict	  =	split['train']
    a		:   Union[str, Any]	  =	split['test']

  # Prepare label mappings.
  # We'll include these in the model's config to get human readable labels in the Inference API.
  a		:   Optional[Any]	  =	dataset['train'].features['labels'].names
  a  ,      a		:   Optional[int]	  =	{}, {}
  for i, label in enumerate(_A							):
    a		:   Dict	  =	str(_A							)
    a		:   Dict	  =	label

  # Load the accuracy metric from the datasets package
  a		:   int	  =	evaluate.load('accuracy'							)

  # Define our compute_metrics function. It takes an `EvalPrediction` object (a namedtuple with a
  # predictions and label_ids field) and has to return a dictionary string to float.
  def compute_metrics(_A							):
    return metric.compute(predictions=np.argmax(p.predictions					,     axis=1							)					,     references=p.label_ids							)

  a		:   Dict	  =	AutoConfig.from_pretrained(
      model_args.config_name or model_args.model_name_or_path					,     num_labels=len(_A							)					,     labelaid=_A					,     idalabel=_A					,     finetuning_task='image-classification'					,     cache_dir=model_args.cache_dir					,     revision=model_args.model_revision					,     use_auth_token=True if model_args.use_auth_token else None					,     )
  a		:   Union[str, Any]	  =	AutoModelForImageClassification.from_pretrained(
      model_args.model_name_or_path					,     from_tf=bool('.ckpt' in model_args.model_name_or_path							)					,     config=_A					,     cache_dir=model_args.cache_dir					,     revision=model_args.model_revision					,     use_auth_token=True if model_args.use_auth_token else None					,     ignore_mismatched_sizes=model_args.ignore_mismatched_sizes					,     )
  a		:   Any	  =	AutoImageProcessor.from_pretrained(
      model_args.image_processor_name or model_args.model_name_or_path					,     cache_dir=model_args.cache_dir					,     revision=model_args.model_revision					,     use_auth_token=True if model_args.use_auth_token else None					,     )

  # Define torchvision transforms to be applied to each image.
  if "shortest_edge" in image_processor.size:
    a		:   int	  =	image_processor.size['shortest_edge']
  else:
    a		:   Tuple	  =	(image_processor.size['height'], image_processor.size['width'])
  a		:   List[Any]	  =	Normalize(mean=image_processor.image_mean					,     std=image_processor.image_std							)
  a		:   Dict	  =	Compose(
      [
          RandomResizedCrop(_A							),
          RandomHorizontalFlip(),
          ToTensor(),
          normalize,
      ]							)
  a		:   int	  =	Compose(
      [
          Resize(_A							),
          CenterCrop(_A							),
          ToTensor(),
          normalize,
      ]							)

  def train_transforms(_A							):
    a		:   List[str]	  =	[
        _train_transforms(pil_img.convert('RGB'							)							) for pil_img in example_batch['image']
    ]
    return example_batch

  def val_transforms(_A							):
    a		:   Any	  =	[_val_transforms(pil_img.convert('RGB'							)							) for pil_img in example_batch['image']]
    return example_batch

  if training_args.do_train:
    if "train" not in dataset:
      raise ValueError('--do_train requires a train dataset'							)
    if data_args.max_train_samples is not None:
      a		:   List[str]	  =	(
          dataset['train'].shuffle(seed=training_args.seed							).select(range(data_args.max_train_samples							)							)
      )
    # Set the training transforms
    dataset["train"].set_transform(_A							)

  if training_args.do_eval:
    if "validation" not in dataset:
      raise ValueError('--do_eval requires a validation dataset'							)
    if data_args.max_eval_samples is not None:
      a		:   Optional[int]	  =	(
          dataset['validation'].shuffle(seed=training_args.seed							).select(range(data_args.max_eval_samples							)							)
      )
    # Set the validation transforms
    dataset["validation"].set_transform(_A							)

  # Initalize our trainer
  a		:   Any	  =	Trainer(
      model=_A					,     args=_A					,     train_dataset=dataset['train'] if training_args.do_train else None					,     eval_dataset=dataset['validation'] if training_args.do_eval else None					,     compute_metrics=_A					,     tokenizer=_A					,     data_collator=_A					,     )

  # Training
  if training_args.do_train:
    a		:   Dict	  =	None
    if training_args.resume_from_checkpoint is not None:
      a		:   List[Any]	  =	training_args.resume_from_checkpoint
    elif last_checkpoint is not None:
      a		:   List[Any]	  =	last_checkpoint
    a		:   Tuple	  =	trainer.train(resume_from_checkpoint=_A							)
    trainer.save_model()
    trainer.log_metrics('train'					,     train_result.metrics							)
    trainer.save_metrics('train'					,     train_result.metrics							)
    trainer.save_state()

  # Evaluation
  if training_args.do_eval:
    a		:   Tuple	  =	trainer.evaluate()
    trainer.log_metrics('eval'					,     _A							)
    trainer.save_metrics('eval'					,     _A							)

  # Write model card and (optionally) push to hub
  a		:   List[Any]	  =	{
      'finetuned_from': model_args.model_name_or_path,
      'tasks': 'image-classification',
      'dataset': data_args.dataset_name,
      'tags': ['image-classification', 'vision'],
  }
  if training_args.push_to_hub:
    trainer.push_to_hub(**_A							)
  else:
    trainer.create_model_card(**_A							)


if __name__ == "__main__":
     main()




'''simple docstring'''






from __future__ import annotations

lowerCAmelCase:   str					=		'Muhammad Umer Farooq'
lowerCAmelCase:   List[str]					=		'MIT'
lowerCAmelCase:   Tuple					=		'1.0.0'
lowerCAmelCase:   List[Any]					=		'Muhammad Umer Farooq'
lowerCAmelCase:   Optional[Any]					=		'contact@muhammadumerfarooq.me'
lowerCAmelCase:   Dict					=		'Alpha'

import re
from html.parser import HTMLParser
from urllib import parse

import requests

class 				a__(   lowerCamelCase__      ):







    def __init__( self					:    Dict		,     __snake_case					:    str      ):
      super().__init__()
      a		:   list[str]	  =	[]
      a		:   List[Any]	  =	domain







    def      lowercase_			( self					:    Dict		,     __snake_case					:    str		,     __snake_case					:    list[tuple[str, str | None]]      ):
      # Only parse the 'anchor' tag.
      if tag == "a":
        # Check the list of defined attributes.
        for name, value in attrs:
          # If href is defined, and not empty nor # print it.
          if name == "href" and value != "#" and value != "":
            # If not already in urls.
            if value not in self.urls:
              a		:   Tuple	  =	parse.urljoin(self.domain		,     __snake_case      )
              self.urls.append(__snake_case      )
def  lowerCamelCase__			(		_A							):
  return ".".join(get_sub_domain_name(_A							).split('.'							)[-2:]							)
def  lowerCamelCase__			(		_A							):
  return parse.urlparse(_A							).netloc
def  lowerCamelCase__			(		_A = "https://github.com"							):
  a		:   Any	  =	get_domain_name(_A							)

  # Initialize the parser
  a		:   Tuple	  =	Parser(_A							)

  try:
    # Open URL
    a		:   List[Any]	  =	requests.get(_A							)

    # pass the raw HTML to the parser to get links
    parser.feed(r.text							)

    # Get links and loop through
    a		:   Union[str, Any]	  =	set()
    for link in parser.urls:
      # open URL.
      # read = requests.get(link)
      try:
        a		:   int	  =	requests.get(_A							)
        # Get the valid email.
        a		:   Optional[Any]	  =	re.findall('[a-zA-Z0-9]+@' + domain					,     read.text							)
        # If not in list then append it.
        for email in emails:
          valid_emails.add(_A							)
      except ValueError:
        pass
  except ValueError:
    raise SystemExit(1							)

  # Finally return a sorted list of email addresses with no duplicates.
  return sorted(_A							)


if __name__ == "__main__":
     lowerCAmelCase:   Any					=		emails_from_url('https://github.com')
     print(F"{len(emails)} emails found:")
     print('\n'.join(sorted(emails)))